
We introduce a novel multi-scale 3D point fusion neural network architecture, which is more lightweight than the existing state-of-the-art depth completion methods while being able to efficiently exploit the geometric constraints provided by a sparse set of 3D points.

We demonstrate state-of-the-art results on NYU-Depth-v2 and KITTI datasets with a network that uses only a fraction of the number of parameters compared to the other recent architectures.

We also show that our method combined with 3D point clouds obtained by using COLMAP [1]}, [2]} outperform recent deep learning based multi-view stereo and structure-from-motion methods both in accuracy and in compactness.

